Live fuel moisture (LFM) is an important fuel property controlling fuel ignition and fire propagation. LFM varies seasonally, and is controlled by precipitation, soil moisture, evapotranspiration, and plant physiology. LFM is typically sampled manually in the field, which leads to sparse measurements in space and time. Use of LFM proxies could reduce the need for field sampling while potentially improving spatial and temporal sampling density. This study compares soil moisture and remote sensing data to field-sampled LFM for Gambel oak (Quercus gambelii Nutt) and big sagebrush (Artemisia tridentata Nutt) in northern Utah. Bivariate linear regression models were constructed between LFM and four independent variables. Soil moisture was more strongly correlated with LFM than remote sensing measurements, and produced the lowest mean absolute error (MAE) in predicted LFM values at most of the sites. When sites were pooled, canopy water content (CWC) had stronger correlations with LFM than normalized difference vegetation index (NDVI) or normalized difference water index (NDWI). MAE values for all proxies were frequently above 20 % LFM at individual sites. Despite this relatively large error, remote sensing and soil moisture data may still be useful for improving understanding of spatial and temporal trends in LFM.